Philippa Kaur

Overall similar genomic and functional repertoires of specialist and generalist wasps
We first sequenced and de novo assembled the reference genomes of Lh and Lb. The generalist species Lh was fully sequenced with PacBio long reads (Supplementary Table 1), which yielded a 487 Mb genome assembly with high continuity (N50 = 2.18 Mb; Supplementary Table 2). The specialist species Lb was sequenced and assembled with 170-fold Illumina read coverage and paired-end sequencing data from five long-insert libraries (up to 13 Kb; Supplementary Table 3). Although the assembled Lb genome had a lower N50 size (480 Kb) and smaller genome size (324 Mb), both the Lh and Lb genomes showed high completeness based on BUSCO and CEGMA assessments (Supplementary Table 2). The difference in genome sizes between Lh and Lb was likely determined by their repeat contents, as 51.47% of the Lh genome was annotated as repeat content in comparison with a 33.79% ratio in the Lb genome (Supplementary Table 4). The average GC contents of the Lh and Lb genomes were 27 and 26%, respectively, indicating that Leptopilina genomes are remarkably AT-rich. Interestingly, unlike the uniform distribution in Lb, the Lh genome shows a secondary peak enriched with scattered genomic windows of remarkably low GC content (16%; Supplementary Fig. 2).
We identified 11,881 and 11,054 protein-coding genes as the official gene sets for Lh and Lb, respectively (Supplementary Table 5). These gene sets were mainly generated by an integrated pipeline, and those retained had evidence from either a full-length transcriptome of pooled developmental stages or high-confidence homology with Insecta genes (see “Methods”). Ortholog analyses across 10 hymenopteran genomes suggested that the genomes of both these parasitoid species maintained a typical hymenopteran gene repertoire (Supplementary Fig. 3). Compared with those of other sequenced insects, the gene numbers of these two Leptopilina species are relatively small, largely due to their low proportions of patchy genes that were not vertically inherited along speciation (Supplementary Fig. 3). We manually annotated several representative gene families or pathways that are strongly associated with the biology of parasitoid wasps (Fig. 1c). The genomes of Leptopilina encode more olfactory receptor (OR) genes than those of other parasitoids except Nasonia vitripennis, while they encode the fewest gustatory receptor (GR) genes. Genes associated with metabolic and immune pathways in Leptopilina were shown to be more or less the same as those in other hymenopteran species. Because consistent patterns within a phylogenetic context were not available, we did not gain informative insights into the gene family variations underlying the divergence between Leptopilina and other Parasitoida species.
The divergence time between the two Leptopilina species was estimated to be approximately 40 Mya, and those between the sublineages of Parasitoida (e.g., Cynipoidea and Chalcidoidea) were estimated to be as distant as hundreds of millions of years (Fig. 1c), consistent with the tremendous diversification of parasitoids17,18. Thus we focused on making detailed comparisons within Leptopilina. The gene repertoires of Lh and Lb shared 8050 (~75%) orthologous pairs (Fig. 1d) with an average sequence identity of 85.6% at the amino acid (aa) level. We calculated the ratio of synonymous-to-nonsynonymous substitutions (dN/dS) to characterize the genes and functional modules associated with the divergence between Lh and Lb. Only four pathways were found to have significantly elevated dN/dS values (Z-test P  More

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A total of 779 persons opened the survey, 486 persons started responding to it, and 308 persons from 40 countries submitted their responses. Seven of these 308 respondents had never heard about biases, and the remaining 301 persons (i.e. 38.6% of those who opened the survey) answered our questions about their attitude to biases.
Nearly all (98%) scientists who responded to our survey were aware of the importance of biases in science. Among these, 33% reported ‘very well’ for their awareness on this topic, 52% classified their awareness as ‘well’ and 13% as ‘poor’. Most of respondents learned about biases from their university courses (36%), from contacts with colleagues (22%) or from scientific literature (20%). Among the different kinds of biases, the best known was observer/observation bias (82%), followed by publication bias (71%) and selection bias (70%); confirmation, reporting/presentation, researcher, measurement, geographic and funding biases were known to 50–60% of respondents (see Appendix S1). Among the seven suggested definitions of biases (see Appendix S1), ‘the tendency to search for, interpret, and publish information in a way that confirms one’s pre-existing beliefs or hypotheses’ corresponded to the understanding of biases by 80% of the respondents, and ‘preferential publication of statistically significant results’ was ranked the second, being selected by 61% of respondents.
In the opinion of our respondents, the stages of scientific research differed in their susceptibility to biases (χ220 = 90.0, P  planning/designing the study  > publishing the outcomes  > reporting the outcomes  > analysing the results  > implementing the study. Similarly, the types of publications were considered prone to biases to different extents (χ216 = 86.8, P  studies based on observational data  > studies based on modelling  > studies based on experiments  > meta-analyses.
Most scientists thought that the severity of the impact of biases on science in general and on their particular research field was medium or high, and a few considered that it was negligible. At the same time, our respondents estimated the impact of biases on their own studies as high almost three times less frequently and as negligible seven times more frequently when compared with their estimates of the impact of biases on other studies within their own research field (Fig. 1).
Figure 1

Impact of biases on science in general, on one’s own research field and on one’s own studies, as estimated by 301 respondents. Bars marked with different letters differ from each other at P  More

The concentration of signal molecule ComX increases by the square of bacterial density
In order to determine the dynamics of signal molecule (ComX) production, we have used experimental and mathematical modeling approaches. We quantified the ComX concentration over time in the spent medium of PS-216 (ΔcomP) with the biosensor strain BD2876 (for strain-description see Supplementary Table 1), which produces β-galactosidase in response to the exogenous addition of ComX34. The assay included proper controls and calibrations to assure the biosensor-derived ComX concentrations are accurate (for details see Materials and methods). We found that the ComX concentration correlated positively with population density of PS-216 (ΔcomP) and remained constant at 10 nM after entering the stationary phase (Fig. 2a). Importantly, the representation of ComX concentration versus cell density (OD650) (Fig. 2b) showed a non-linear trend between the two parameters. The experimental data were fitted by an allometric function:

$$SMleft( t right) = aNleft( t right)^b$$
(1)

Where SM(t) is a signal molecule (ComX) concentration in time, N(t) is bacterial cell density in time, expressed as optical density of the bacterial suspension (OD650). The fitting results for parameters a and b were (9.6 ± 0.6) nM a.u.−2.09 and 2.09 ± 0.10, respectively. The value of parameter a means that at OD650 = 1.0 a.u., which corresponds to the stationary growth phase in our experimental conditions and the bacterial density of 4 × 108 cells mL‒1, the ComX concentration is about 10 nM. In the early exponential growth phase the concentration was about 0.1 nM. Considering parameter b, the value obtained (2.09 ± 0.10) indicates that the ComX concentration increases by the square of bacterial density. This means that with increasing population density, the SM concentration (ComX) increases by the second power, while the amount of ComX per cell increases linearly. This relationship suggests that ComQXPA has an ultra-sensitive encoder module9, where signal molecule production is very sensitive to cell density. The same mathematical relationship can be obtained by assuming that SM production rate per cell corresponds to the product of a specific cell growth rate and a cell density (i.e., population growth rate, for details, see Supplementary Methods, Derivation of ComQXPA communication system model). The dependence of the SM production rate per cell on (a) cell density and (b) the specific cell growth rate can be seen as an alternative way to obtain the ultra-sensitivity of encoders, which is usually achieved by SM dependent positive feedback in many QS systems9. This makes ComX a true indicator of population density, which also encodes information about the cell growth rate.
Fig. 2: The accumulation of signal molecule (SM) during the growth of B. subtilis and fitness cost of SM production.

a The growth curve (OD650) of B. subtilis PS-216 ΔcomP (no signal receptor) producing SM (ComX) that is accumulating in the growth medium of fermenter working in the batch mode of n = 3 biologically independent replicates is presented; b The experimental data n = 3 biologically independent replicates where the data ≥ limit of detection of SM was fitted by Eq. (1); the error bars for SM concertation are standard errors calculated from 8 technical replicates for each biological replicate; c The comparison of growth curves of B. subtilis PS-216 with no signal molecule receptor (ΔcomP) and no signal molecule production and receptor (ΔcomQXP) of n = 3 biologically independent replicates; the OD650 at t = 0 h was corrected with respect to the measured CFU of the inoculum. The slopes of the fitted lines in c correspond to the growth rate divided by log 2; the exponential phase points in the most reliable OD650 region ( >0.1 a.u. and  1.75 h, P  1 values obtained for all fits indicate positive cooperativity (i.e., ultrasensitivity14) in the binding of the transcriptional factor ComA to the srfA promoter43,44. This agrees with the research showing that two molecules of the ComA homodimer cooperatively bind to the two promoter regions located upstream of the RNAP binding sites of srfA13,20,45. The inactivation of the second promoter region decreases the promoter activity of srfA by 100-fold (ref. 13), which underscores the importance of the second binding region, explains n ≥ 2 and the sharp increase in srfA promoter activity with ComX concentration. In addition, we show here that the critical concentration of ComX required to induce quantifiable response (designated here as lower limit response (LLR)) is 0.2–0.5 nM. These results, therefore, suggest that the response per cell depends cooperatively on the ComX concentration and that the cells respond to very low concentrations of ComX.
Fig. 4: Influence of oxygen on the presence of SM (ComX).

a Signal molecule deficient B. subtilis PS-216 (∆comQ, PsrfAA-yfp) was incubated in the presence of SM for 4 h and the maximum normalized response was determined from the activity of the srfA promoter. n = 4 biologically independent replicates were performed. Best, concatenated fit to the model in Eq. (2) is presented together with 95% confidence level. b the logistic fit to one of the three growth curves (n = 3, biologically independent replicates) measured as OD650 of the culture B. subtilis PS 216 (srfA-lacZ) producing signal molecule, SM that accumulated in the growth medium of batch system is shown. The response per cell data, obtained as the β-galactosidase activity of srfA promoter of B. subtilis PS-216 (srfA-lacZ) was fitted by Eq. (3), (R2  > 0.99). The time interval at which SM concentration is high enough to cause the measurable response, i.e., lower limit of response (LLR) as predicted from data in experiments in (a) is given as dashed window in (b). One of the five qualitatively and quantitatively similar experimental results is presented. Error bars represent SD of 8 technical replicates. For fits of additional replicates and data variability refer to Supplementary Tables 3 and 4.

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Fully functional ComQXPA communication system does not require a positive feedback loop–the validation of the ComQXPA communication system
The response curve in Fig. 4a is a function of the SM concentration only. In the more natural setting (i.e., during growth) the cells encounter growth-dependent changes in SM concentrations as well as changes in bacterial density and growth rate over time. We have therefore asked whether the response curve based on the modeling and results presented in Figs. 2b and  4a could fit the response data in the SM producing and responding strain exposed to changes in these three parameters.
We cultivated the SM producing and responding PS-216 strain carrying the response reporter (PsrfA-lacZ) in a large volume bioreactor system (Fig. 4b). This allowed sterile sampling of spent medium and cells (for response quantification) at several time points, without affecting growth conditions. Immediately after the inoculation of the fresh medium by overnight culture the β-galactosidase activity of PS-216 (PsrfA-lacZ) was high. We assumed that this was a consequence of the accumulation of the expressed PsrfA reporter (β-galactosidase, RM) during the overnight growth. As a consequence of the dilution of the intracellular β-galactosidase (RM) due to cell division, the activity of the β-galactosidase decreased sharply after 2 h incubation (Fig. 4b). Simultaneously, as predicted by (Eq. 1), the concentration of SM (ComX) in the medium was increased exponentially during growth, and soon reached a critical concentration to activate the srfA promoter. In particular, as elucidated by the fits of (Eq. 2) to the data in Fig. 4a, the lower limit of the response (LLR) is reached shortly before upturn of the cell response curve in Fig. 4b. At this point the culture is in exponential growth phase at the cell density of 3 to 8 × 107 cells mL−1. The steep slope of the response curve indicates that the rate by which the response molecule (RM) is synthesized now exceeds the dilution due to the cell division rate. From now on, the response per cell correlates approximately linearly with OD650, suggesting a strong coupling to cell growth. Taking these facts into account and considering that the response molecule (RM) concentration is sensitive to the concentration of the signal, SM, (Eq. 2) and that SM can be expressed in terms of cell density (Eq. 1), the concentration of a response molecule per cell, RM(t)/N(t), can be analytically described (see also Supplementary Methods, Derivation of ComQXPA communication system model) as:

$$frac{{RM(t)}}{{N(t)}} = frac{{RM0}}{{N(t)}} + frac{{RM1(t)}}{{N(t)}}$$
(3)

where RM0/N(t) is the response per cell of overnight culture, i.e., the overnight accumulated β-galactosidase. The second term, RM1(t)/N(t) accounts for the synthesis of the β-galactosidase after inoculation of a fresh medium and comprises the parameters describing the sensitivity of the response to a signal molecule, Wmax, Km, n (Eq. 2), the signal production, a (Eq. 1), cell density, N(t) and proportionality constant, k that gives the magnitude of the response per cell when the potential to respond to the signal is maximally fulfilled (i.e., at Wmax) and the specific growth rate is 1 h−1. The definition of RM1(t) is given in Supplementary Methods (eq S11). Note that for the derivation of Eq. (3) we assumed no degradation of SM occurs, as our experiments suggest SM was stable under the conditions studied (Supplementary Figs. 6a and 7, see also Supplementary Methods, Derivation of ComQXPA communication). All the parameters in (Eq. 3), except k in RM1(t) and RM0, were taken as constants obtained in the independent experiments by fits of (Eq. 1) and (Eq. 2). With k and RM0, as the only fitting parameters, we applied the mathematical model in (Eq. 3) (for details of the model equation see Supplementary Methods, Derivation of ComQXPA communication system model) to fit the experimental cell response data (Fig. 4b). The successful fit (see Supplementary Table 4 for details) indicates that the relationship assumed among cell density, cell growth, signal concentration and response in (Eq. 3) is valid and yields (760 ± 120) M.U. for k and (5.5 ± 1.5) M.U. a.u. for RM0. Again, we did not need to incorporate the SM feedback loop into our model, which is consistent with published results suggesting that this communication system lacks a feedback loop10,11,12,13.
The ComQXPA dependent signaling and response at the cellular level
So far, we have focused on the population averages, which is a traditional approach in studies on microbial communication systems17,46. We here report results on communication dynamics of B. subtilis at the single cell level using fluorescence-based molecular tools. This approach provides the means to track temporal changes in expression of genes involved in signal synthesis (signaling) and in response and thus provides the insight into a phenotypic heterogeneity within the population.
We used the double-labeled fluorescencent strain B. subtilis PS-216 (comQ-yfp, srfA-cfp), in which fluorescent reporters were fused to the comQ and srfA promoters. The two genes code for the ComX signal-processing protein and the communication-activated operon, respectively. Since comQ and comX share the same promoter and their genetic sequences often overlap15 expression level of comQ corresponds to the expression level of comX. The fluorescence of individual cells was observed under the microscope in different growth phases and quantitatively analyzed (Fig. 5).
Fig. 5: Single cell quantitative fluorescence microscopy of B. subtilis PS-216 (comQ-yfp, srfA-cfp).

The fluorescence microscopy images were taken periodically during incubation of B. subtilis PS-216 (comQ-yfp, srfA-cfp) in a batch fermenter by the YFP filter (a), CFP filter (b) or DIC (c). The example YFP and CFP images, taken after 3 h of incubation were pseudo-colored. The scale bar represents 10 µm. n = 3 biologically independent experiments were performed. d % of population of cells that are hyper-expressing comQ-yfp or srfA-cfp is depicted. Gene expression level determined by single cell fluorescence microscopy was measured as Na-fluoresceinate standard normalized mean fluorescence intensity per cells expressing comQ-yfp (e) and srfA-cfp (f); ON is the overnight culture. One of the three qualitatively similar cell distributions is shown in (g) and (h) for comQ-yfp and srfA-cfp, respectively; areas under the curves are the same in all time points. For additional replicate see Supplementary Fig. 8.

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The observed expression pattern for the signaling gene (comQ-yfp) follows lognormal distribution (Fig. 5g). A small number of outliers in the comQ expression (on average, 10x brighter than the majority) were easily detected in the qualitative image analysis (Fig. 5a). These hyperproducers were not present in the overnight culture and began to occur during exponential growth, after 1-hour incubation in fresh medium, (Fig. 5d). In general, hyperproducers accounted for about 0.1–1% of the population, and their frequency increased during the first 6 hours. These data suggest that bulk of the ComX is nevertheless produced by the majority of the population as expected for the true QS communication systems. The contribution of the srfA hyperproducers to the total surfactin production is even less pronounced since their occurrence did not exceed 0.1% of the population (Fig. 5b, d).
The most heterogeneous expression of the communication signaling gene (comQ-yfp) was observed in overnight culture, immediately after its transfer to the fresh medium (Fig. 5g, Supplementary Fig. 8a), but hyperproducers where not detectable at this time (Fig. 5d). Once the cells begun to divide, the distribution shifted to lower fluorescence intensities with a simultaneous decrease in heterogeneity, but from 3 to 4 h onwards single cell fluorescence gradually increased, along with an increase in population heterogeneity (Fig. 5e, g, Supplementary Fig. 8a). This suggests that the expression rate is now higher than the division rate (i.e., the production overpowers the dilution due to cell division). A similar pattern was observed in the communication response (srfA-cfp) (Fig. 5f, h, Supplementary Fig. 8b) with two major differences. The minimum level in comQ-Yfp fluorescence was reached 1 h later than srfA-Cfp fluorescence, which suggests the expression of ComX is in first hours low compared to the srfA expression. Nevertheless, the entire cell population, with the exception of hyperproducers, which represented only a fraction of the comQ/srfA expressing cells, followed unimodal lognormal distribution expression pattern. This suggests that the ComQXPA communication phenomenon in B. subtilis, at least under the conditions in our experiment, is not restricted to individuals and can be studied at the population level, i.e., the averages represent well the population.
The comQ-yfp and srfA-cfp expression co-localization analysis (Supplementary Table 5) reveals the correlation coefficient of about 0.5, which is significantly (P = 0.01) higher than in the overnight culture. The presence of the correlation suggests that on average, cells that produce the signal more intensively, also respond to signal more intensively, supporting the idea of self-sensing47. However, the correlation coefficient strength was only moderate, suggesting that sensing of the external signal (sensing-of others) still works as expected for a typical QS system.
The induced response in ComQXPA communication system is graded and almost switch-like
The perfect QS system does not produce a response until the threshold bacterial density is reached and then immediately switches to a full response. This minimum to maximum transition may be either a perfect switch or a graded induction. By combining the information from Figs. 2b and 4a in the form of eq S9 results in the normalized ComQXPA response curve as a function of bacterial density (Fig. 6a) that resembles a graded switch like induction (compare to Fig. 1a). The perfect switch like communication system is unrealistic, because it requires that all the cells are perfectly synchronized and immediately switch to maximal response, leaving no time for the adaptation to the signal stimuli. It is reasonable to expect that for the true quorum sensing system (QS) most of the response has to occur within the same generation of dividing cells (ngen  More

Study design
This study began in 2013, before P. destructans invaded Michigan and Wisconsin, and continued through 2020, after all Michigan and Wisconsin hibernacula were invaded by the pathogen. Throughout this period, we visited 22 hibernacula twice per winter during bat hibernation to quantify bat colony sizes, individual bat roosting temperatures, fungal loads (to which we added a constant 0.0001 before transforming to the log10 scale), and recapture probabilities (Fig. 2). We sampled bats during early hibernation in November, when more than 95% of swarm activity was expected to be finished51, and again during late hibernation in March, when less than 1% of spring emergence activity was expected to have begun51,52. For each sampling event, we counted all bats of all species within the site. We focused our analyses on the little brown bat (Myotis lucifugus). This species suffered severe declines due to WNS24,53, and was the only species abundant enough to provide sufficient sample sizes for our analyses. We divided bat counts by sections within the hibernaculum (i.e., “rooms”) that potentially varied in microclimate, and we used HOBO Pro v2 data loggers to continuously record temperatures every 3 h in these sections (Supplementary Figs. 2 and 7).
After counting all bats, we haphazardly sampled 20–25 individual little brown bats stratified across sections and roughly in proportion to the number of bats in each section (Fig. 2). For each sampled bat, we used a Fluke 62 MAX IR laser thermometer to quantify the temperature of the substrate directly adjacent to the bat (2 years is defined here as the post-invasion period) and that we had sampled during both the pre-invasion period (up to and including year 0, when WNS was first detected) and invasion period (years 1–2 of invasion). From pre-invasion to post-invasion, bat temperature distributions might have changed across these 12 hibernacula via two mechanisms: (1) bat preferences could have remained the same while populations declined in hibernacula and/or microsites with unfavorable temperatures, causing distribution shifts via selection; and/or (2) bat preferences could have changed towards avoidance of hibernacula and/or microsites with high temperature-mediated disease mortality, causing distribution shifts via selection or learning. In both cases, we would expect the average regional bat roosting temperatures to change over time. In contrast, we would only expect the average bat roosting temperature in any given site (or section) to change over time if the site provided a wide range of temperatures to select from, which was not always the case in the hibernacula we studied. Therefore, we quantified how the regional mean early hibernation roosting temperatures for all counted bats changed across all 12 of these hibernacula from pre-invasion to post-invasion (2013–2020, up to four years after the fungus was first detected) using a Gaussian regression, without including a random site effect. We visualized this change using density plots with 1 °C smoothing bin widths.
Though we stratified bat roosting temperature sampling by section, roughly in proportion to the total number of bats present in a section, we only sampled up to 25 little brown bats per site, and we could not sample exactly in proportion to the total bats present. Therefore, calculating the mean temperatures based on only sampled bats might be misleading. Instead, we used the observed temperatures from our sampled bats to estimate the roosting temperatures for bats that were counted but not sampled. In particular, we randomly generated an additional set of temperatures for all unmeasured, counted bats based on the mean and standard deviation of the roosting temperatures of sampled bats in the same section during the same survey. We then used both known (sampled bats) and estimated (counted bats) temperatures in our Gaussian regression to determine how average temperatures changed from pre-invasion to post-invasion. We also confirmed that qualitative results were the same if we only used the temperatures from sampled bats, without estimation. By considering how bats’ early hibernation temperature distributions changed throughout invasion, regardless of infection status, we were quantifying bat roosting preferences within and between hibernacula, rather than behavioral responses to infection within a season (i.e., “behavioral fever”).
Finally, we used the number of bats counted per hibernaculum during early hibernation (November) and late hibernation (March) in all hibernacula that still had bats in post-invasion years to calculate two demographic parameters: over summer population growth rates (reflecting immigration and recruitment to sites between March and November) and over winter mortality rates (population growth rates from November to March). We used a Gaussian linear model to determine whether the log10 population growth rates were correlated with average early hibernation roosting temperatures for sampled bats in each hibernaculum, the demographic season (over summer versus over winter), and the interaction between temperature and demographic season. A significant interaction term would indicate that temperature-correlated bat immigration and recruitment rates were decoupled from temperature-mediated population declines.
All analyses described above were performed in R version 3.5.161 with packages ‘ggplot2’62, ‘lme4’63, ‘loo’64, ‘pROC’65, and ‘R2jags56. Visual inspection of residuals and predictions plots confirmed acceptable model fits for all regression models.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

Chemicals and reagents
Sodium chloride (S3014), potassium chloride (P9541), sodium phosphate dibasic (S3264), potassium phosphate monobasic (P9791), potassium bicarbonate (12602), ammonium chloride (A9434), ethylene diamine tetra acetic acid di-sodium salt (E6635), bovine serum albumin (BSA) (5482), absolute ethanol (100983), methanol (34860) were purchased from Sigma–Aldrich, St. Louis, MO, United States. CD 34, Sca 1, and CBA Flex Set which contains IL-3 (558346), IL-6 (558301), TNFα (558299), IFN γ (562233), G-CSF (560152), GM-CSF (558347), IL-1α (560157), IL-1β (560232), Mouse/Rat Soluble Protein Master Buffer Kit (558266) were procured from BD Biosciences, United States. May-Grunwalds Stain (S039) and Giemsa Stain Solution (TCL083) were procured from Hi-Media India.
1 L 1X PBS (8 g of sodium chloride, 0.2 g of potassium chloride, 1.44 g of sodium phosphate dibasic, 0.25 g of potassium phosphate monobasic to 1 L at pH 7.4), 1 L 1X RBC lysis buffer (1 g of potassium bicarbonate, 8 g of ammonium chloride and 0.03 g of di-sodium EDTA), 1% BSA in PBS, 70% ethanol in PBS, and may-grunwald-giemsa stain mixed in 3:1 ratio were prepared in the laboratory.
Animals and groupings
Pathogen free Strain ‘A’ male mice weighing 25.0–30.0 gm were received from Institute of Nuclear Medicine and Allied Science (INMAS) experimental animal facility at the age of 9 weeks, which is equivalent to young adult humans. Six mice per cage were housed under 25 ± 3 °C temperature and relative humidity of 30–70% in 12 h light/dark cycle with standard food and water. The study design strictly adhered to the guidelines approved by Institutional Animal Care and Use Committee (IACUC) of our institute, Institute of Nuclear Medicine and Allied Sciences (INMAS), Defence Research and Development Organization (DRDO), Delhi, India (Institute Animals Ethics Committee number: INM/IAEC/2019/01).
After 1 week of acclimatization under ambient conditions, mice were grouped for the survival assay according to localized gamma radiation dose i.e. 7, 9, 10, 12, 15, 17 and 19 Gy. Eighteen animals were assigned in each group for this assay.
For all other parameters mice were divided into two groups with six animals in each. Group 1, consisted of untreated or sham irradiated animals. Group 2 was exposed to 15 Gy localized irradiation. Mice were sacrificed at different time points (1, 2, 4, 7, 10, 15, 20, 25, and 30 days) post treatment. Hematopoietic stem cell marker CD 34 and Sca1 were measured on 1, 4, 7, and 10th day post-irradiation.
Irradiation
A Cobalt-60 teletherapy (Model: Bhabhatron-II, Panacea Medical Technologies Pvt Ltd, India) machine was used to irradiate the hinds of the three mice at a time (Fig. 1). The cobalt-60 beam was calibrated following IAEA TRS-398 protocol, with measurement done in water phantom (30 cm × 30 cm × 30 cm), for a field size 10 cm × 10 cm at source to surface distance (SSD) of 80 cm. A Farmer type, 0.6 cc volume, ionization chamber was utilised and the same was recently calibrated for absorbed dose to water (NDW) at Secondary Standard Dosimetry Laboratory (SSDL) of India. The measured absorbed dose rate (water, 10 cm × 10 cm, 80 SSD, Dmax) was 1.39 Gy/min.
Figure 1

Representative image of animal exposure with γ-rays in a field size of 2 cm × 35 cm.

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The localized bone marrow irradiation was done using single Posteroanterior (PA) beam with 2 cm × 35 cm field size at 80 cm SSD opened through secondary collimators of the machine. The 5 mm thick acrylic re-strainers were used to immobilize each mouse which also provides the build-up thickness for the irradiation. The dose prescription point was the surface of the hinds i.e. 5 mm below the surface of the re-strainer. The whole irradiation platform was placed on 5 cm thick acrylic slab to provide sufficient back scatter and all the air gaps between the three mice were filled with tissue equivalent gel bolus or wet cotton to compensate for missing side scatter. The prescription doses were absorbed dose to water and not corrected for any tissue heterogeneity.
The absolute and relative dosimetry of collimated field (2 cm × 35 cm) were performed using pin point (0.04 cc volume) ionization chamber and EBT3 (ISP, Wayne, USA) Gafchromic dosimetry films in solid water phantom. The response of the pin point chamber and EBT3 films was calibrated against the SSDL calibrated ionization chamber in solid water phantom at the depth of 5 cm and field size 10 cm × 10 cm in the same beam quality (i.e. cobalt-60). The film scanning was performed using Epson 10000XL flatbed scanner as per the manufacturers scanning protocol. The beam profiles were obtained from the scanned films and the full width at half maximum (FWHM) in transverse direction was 1.78 cm for the opening of 2.0 cm. The dose uniformity was better than 4% in central 80% of the field. The absolute dose rate (dose to water, dmax, 2 cm × 35 cm, SSD 80 cm) measured at the centre of the field was 1 Gy/min. The overall uncertainty in dose calculations is 6.5% with enhancement or coverage factor (k = 2) at 95% confidence. The main body of the mice was covered with 15 mm lead to prevent any scatter radiation reaching at other body parts of the animal. The surface dose reaching at the shielded body parts of the mice was measured using 0.4 cc ionisation chamber. The measured mean surface dose was 0.024 ± 0.014% of the open field dose. This dose is negligible and considered to be acceptable.
Survival assay in mice
For survival assay, 18 mice from each experimental group i.e. 7, 9, 10, 12, 15, 17 and 19 Gy, were irradiated at a fixed dose rate of 1 Gy/min. Groups were inspected daily for their morbidity and mortality status for a total duration of 30 days. All the surviving mice were euthanized humanely using intra-peritoneal injection (i.p.) with dexmedetomidine (0.3 mg/kg) in combination with ketamine (190 mg/kg) at the completion of the observation period.
Validation of hematopoietic syndrome model
The model of hematopoietic syndrome was validated with localized radiation of 15 Gy as this was the maximum dose at which all the animals survived as observed in the survival results. Mice were euthanized using intra-peritoneal injection (i.p.) with dexmedetomidine (0.3 mg/kg) in combination with ketamine (190 mg/kg) at 1, 2, 4, 7, 10, 15, 20, 25, and 30th day time points. Further analysis such as organ weight, cell counting, and serum cytokines measurement assays were conducted on various organs such as femur bone, spleen, thymus, lungs, kidneys (L), kidneys (R), liver and heart and blood at above mentioned time points. Hematopoietic stem cell markers (CD 34 and Sca 1) were measured in bone marrow cells of mice at 1, 4, 7, and 10th day time points.
Body and organ weights
The control and 15 Gy locally irradiated group animals were sacrificed at 1, 4, 7, 10, 15, 20, 25, and 30th day time points. The organs (spleen, thymus, lungs, kidneys (L), kidneys (R), liver and heart) were dissected out and weighed individually. Relative organ weight was calculated as the ratio between organ weight and body weight.
Haematology
The blood samples were collected from untreated and 15 Gy localized radiation group at 1, 4, 7, 10, 15, 20, 25, and 30th day time points in EDTA vial by cardiac puncture. 20 µl of collected blood from each experimental animal was taken for haematology and analysed using Nihon Kohden Celltac α, Tokyo, Japan, a fully automatic 3 part haematology analyzer.
Bone marrow cell counting
Total bone marrow cells were flushed out at 1, 4, 7, 10, 15, 20, 25 and 30th day points in 1 ml PBS from both femurs of the untreated mice and 15 Gy localized radiation group. The PBS containing cells was centrifuged at 2000 rpm for 8 min and the supernatant was discarded. The cells were washed with 1 ml PBS and the cell pellet was re-suspended in 1 ml of PBS. The re-suspended sample was diluted as required. The cells were then counted using improved Neubauer chamber under Olympus BX-63 microscope.
Bone marrow smears study
Bone marrow cells collected at 1, 4, 7, 10, 15, 20, 25, and 30th day post-irradiation from different treatment groups were centrifuged at 2000 rpm for 8 min and re-suspended in 50 μl of Fetal Bovine Serum (FBS). A small drop of cells suspension was dropped on a clean microscopic slide and then smeared thinly over an area of 2–3 cm by pulling another glass slide held at a 45° angle. Cells were fixed with methanol and stained with may-grunwald-giemsa. Slides were then analysed using Olympus BX-63 microscope.
Splenocytes and thymocytes counts
The organs excised at 1, 4, 7, 10, 15, 20, 25, and 30th day time-points were cleaned in chilled PBS. Using sterile-chilled frosted slides both the thymus and spleen were minced into a cell suspension and which was further centrifuged at 2000 rpm for 8 min. After centrifugation the supernatant was discarded. The RBCs were lysed using RBC lysis buffer and the cells were washed with 1 ml PBS. The cells were then counted using improved Neubauer chamber under Olympus BX-63 microscope.
Measurement of hematopoietic stem cell marker CD 34 and Sca1
Bone marrow cells were isolated from both the femurs from various treatment groups at different time points. Cell were washed with chilled PBS and fixed with 70% chilled ethanol and stored overnight at – 20 °C. Following day, five million cells were blocked with 1% BSA-PBS and stained with CD 34 and Sca 1 cell-surface markers for 20 min at room temperature. 10,000 cells from each sample were analyzed using FACS LSR II (BD Biosciences, San Jose, CA) and results were visualized by the BD FACSDiva V7 software (BD Biosciences, San Jose, CA).
Cytokines expression changes
Mice blood samples were collected in BD serum separation tube at different time-points post treatment (1, 4, 7, 10, 15, 25, and 30 days) via. cardiac puncture from different treatment groups. Samples were incubated at room temperature for 2 h and after incubation centrifuged at 6000g for 15 min. Serum was separated and stored at − 80 °C until analysis. The Levels of IL-3, IL-6, TNFα, IFN γ, G-CSF, GM-CSF, IL-1α,and IL-1β cytokines in mice serum were determined by using respective BD Cytometric Bead Array (CBA) Flex Set (BD Biosciences, USA) on dual laser flow analyzer (LSR-II, Becton Dickinson Biosciences, USA) according to manufacturer’s instructions. Results were analysed using FCAP Array V3 software (BD Biosciences, San Jose, CA).
Statistical analysis
All values obtained in our results are represented as mean ± SD of three independent replicates. The 30 day survival data was plotted using Kaplan–Meier analysis. The difference between the experimental groups was evaluated by one-way analysis of variance, with Newman–Keuls multiple comparison test (V, 8.01; GraphPad Prism, San Diego, CA, USA). The comparisons were made among the untreated and 15 Gy locally irradiated groups for all experimental parameters except the survival study. A value of p  More